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Association between Proton Pump Inhibitor Therapy andClostridium difficile Infection: A ContemporarySystematic Review and Meta-AnalysisImad M. Tleyjeh1,2,3,4*, Aref A. Bin Abdulhak5., Muhammad Riaz6., Faisal A. Alasmari1, Musa A. Garbati1,

Mushabab AlGhamdi1, Abdur Rahman Khan7, Mohamad Al Tannir6, Patricia J. Erwin8, Talal Ibrahim9,

Abed AlLehibi1, Larry M. Baddour3, Alex J. Sutton10

1 Department of Medicine, King Fahad Medical City, Riyadh, Saudi Arabia, 2 College of Medicine, AlFaisal University, Riyadh, Saudi Arabia, 3 Division of Infectious Diseases,

Mayo Clinic, Rochester, Minnesota, United States of America, 4 Division of Epidemiology, Mayo Clinic, Rochester, Minnesota, United States of America, 5 Department of

Internal Medicine, University of Missouri-Kansas City, Kansas City, Missouri, United States of America, 6 Research and Scientific Publication Center, King Fahad Medical City,

Riyadh, Saudi Arabia, 7 Department of Internal Medicine, University of Toledo Medical Center, Toledo, Ohio, United States of America, 8 Mayo Medical Library, Mayo Clinic,

Rochester, Minnesota, United States of America, 9 Department of Orthopedic and Trauma Surgery, Hamad Medical Corporation Doha, Qatar, 10 Department of Health

Sciences, University of Leicester, University of Leicester, Leicester, England

Abstract

Introduction: Emerging epidemiological evidence suggests that proton pump inhibitor (PPI) acid-suppression therapy isassociated with an increased risk of Clostridium difficile infection (CDI).

Methods: Ovid MEDLINE, EMBASE, ISI Web of Science, and Scopus were searched from 1990 to January 2012 for analyticalstudies that reported an adjusted effect estimate of the association between PPI use and CDI. We performed random-effectmeta-analyses. We used the GRADE framework to interpret the findings.

Results: We identified 47 eligible citations (37 case-control and 14 cohort studies) with corresponding 51 effect estimates.The pooled OR was 1.65, 95% CI (1.47, 1.85), I2 = 89.9%, with evidence of publication bias suggested by a contour funnelplot. A novel regression based method was used to adjust for publication bias and resulted in an adjusted pooled OR of 1.51(95% CI, 1.26–1.83). In a speculative analysis that assumes that this association is based on causality, and based on publishedbaseline CDI incidence, the risk of CDI would be very low in the general population taking PPIs with an estimated NNH of3925 at 1 year.

Conclusions: In this rigorously conducted systemic review and meta-analysis, we found very low quality evidence (GRADEclass) for an association between PPI use and CDI that does not support a cause-effect relationship.

Citation: Tleyjeh IM, Bin Abdulhak AA, Riaz M, Alasmari FA, Garbati MA, et al. (2012) Association between Proton Pump Inhibitor Therapy and Clostridium difficileInfection: A Contemporary Systematic Review and Meta-Analysis. PLoS ONE 7(12): e50836. doi:10.1371/journal.pone.0050836

Editor: Markus M. Heimesaat, Charite, Campus Benjamin Franklin, Germany

Received August 23, 2012; Accepted October 24, 2012; Published December 7, 2012

Copyright: � 2012 Tleyjeh et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The authors have no funding or support to report.

Competing Interests: The authors have declared that no competing interests exist.

* E-mail: [email protected]

. These authors contributed equally to this work.

Introduction

Proton pump inhibitors (PPIs) are one of the most prescribed

groups of drugs globally [1]. PPIs are effective for the treatment of

all acid-related disorders. They are also indicated ICU patients

with coagulopathy, patients on mechanical ventilation, and

patients with history of peptic ulcer disease, (particularly those

on NSAID or antiplatelet therapy) [2].

The use of PPIs has increased dramatically [1] despite concerns

that PPIs are overprescribed both in primary care [3] and in

hospitals, both in the in-patient setting [4–7] and on discharge [8].

Moreover, concerns have been raised about the potential long-

term effects of these drugs. PPIs have been associated with

significant interaction with other drugs [9,10] and fractures [11],

interstitial nephritis [12], pneumonia [13] and enteric infections

[14,15], namely Clostridium difficile infection (CDI).

CDI has recently emerged as a major public health problem

with current estimates suggesting a point prevalence of 13.1/1000

in-patient population [16]. Studies have reported increases in both

incidence and mortality of CDI [17–20]. The increase in incidence

of CDI has been attributed to an aging population, increase in use

of antibiotics and acid suppressive drugs. PPIs are postulated to

increase the proliferation of spores and change the acidic milieu of

the stomach that permits spores to survive intraluminally.

The role of gastric acid suppression therapy has gained much

interest recently as a risk factor for CDI. Four recently published

meta-analyses have suggested an association between gastric acid

suppression therapy with proton pump inhibitors (PPI) and CDI

PLOS ONE | www.plosone.org 1 December 2012 | Volume 7 | Issue 12 | e50836

[15,21,22,23]. The United States Food and Drug Administration

(FDA) recently warned the public about a possible association

between CDI and PPI use [19]. Nevertheless, these reviews had

important limitations such as missing a large number of published

studies [15,19,22,23], using only unadjusted data from observa-

tional studies [15,22,23], not exploring heterogeneity and the

effect of publication bias and over-interpreting the findings. We,

therefore, performed a systematic review and meta-analysis that

addressed the role of PPIs in CDI. We used the MOOSE [24] and

PRISMA guidelines [25] for reporting systematic reviews. We

include new studies published after the previous meta-analyses and

added unique approaches to adjust for publication bias as well as

explore the potential effect of unknown confounders. We use the

Grades of Recommendation, Assessment, Development and

Evaluation (GRADE) framework [26] to interpret our findings.

Methods

Study Search StrategyThe search strategy and subsequent literature searches were

performed by a medical reference librarian (PJE) with 38 years of

experience. The initial strategy was developed in Ovid MEDLINE

(1990 through January 2012), using MeSH (Medical Subject

Headings) controlled vocabulary, and then modified for Ovid

EMBASE (1990 through January 2012). The search was intended

to capture all acid suppression studies. Primary terms were:

enterocolitis, pseudomembranous/AND the therapeutic agents of

interest: explode omeprazole, explode proton pump inhibitors,

anti-ulcer agents, and explode histamine H2 antagonists (Explode

allows including all of the specific drugs, without having to use all

of the various terms, synonyms, brands and generic names.)

Articles were limited to randomized controlled trials, cohort

studies, and/or case-control studies. The same process was used

with Ovid EMBASE with alterations as necessary to accommodate

EMBASE’s more granular subject headings. ISI Web of Science

and Elsevier Scopus use textwords: (difficile OR pseudomembra-

nous OR pseudo-membranous) AND (omeprazole OR ‘‘proton

pump’’ OR ranitidine OR h2 OR h-2 OR ‘‘acid suppression’’ OR

antacid*)) AND (random* OR trial* OR blind* OR cohort* OR

controlled OR prospective).

There was no restriction on language. All results were downloaded

into EndNote 7.0 (Thompson ISI Research soft, Philadelphia,

Pennsylvania), a bibliographic database manager, and duplicate

citations were identified and removed. Two authors (A.B.A. and

F.A.) independently assessed the eligibility of identified studies.

Study SelectionTo be included, a study had to: (1) be an analytical study; and (2)

have examined the association between PPI use and incidence of CDI.

Data CollectionA data collection form was developed and used to retrieve

information on relevant features and results of pertinent studies.

Two reviewers (A.B.A. and F.A.) independently extracted and

recorded data on a predefined checklist. Disagreements among

reviewers were discussed with two other reviewers (I.M.T. and

M.A.), and agreement was reached by consensus. Data included

the following: study characteristics (i.e., country and year of study),

characteristics of the study, PPI intake definition and ascertain-

ment, and outcome. We also collected adjusted effect estimates

and 95% confidence intervals (CI) based on the multivariable

regression model used in each study, and the list of variables

considered for inclusion in the multivariate analysis.

We used the Newcastle-Ottawa Quality Assessment Scale for cohort

and case-control studies [27] which is intended to rate selection bias,

comparability of the exposed and unexposed groups of each cohort,

outcome assessment, and attrition bias. Two reviewers (A.B.A. and

F.A.) independently assessed the methodological quality of selected

studies using the Newcastle-Ottawa Quality Assessment Scale for

cohort and case-control studies. Disagreement among reviewers was

discussed with 2 other reviewers (I.M.T. and M.A.), and agreement

was reached by consensus.

We used the GRADE framework to interpret our findings. The

Cochrane Collaboration has adopted the principles of the

GRADE system [28] for evaluating the quality of evidence for

outcomes reported in systematic reviews.

For purposes of systematic reviews, the GRADE approach

defines the quality of a body of evidence as the extent to which one

can be confident that an estimate of effect or association is close to

the quantity of specific interest. Quality of a body of evidence

involves consideration of within-study risk of bias (methodological

quality), directness of evidence, heterogeneity, precision of effect

estimates and risk of publication bias.

Statistical AnalysesMeta-analyses. The primary effect measures used in the

meta-analysis were Odds Ratios (OR) (46 observations), and

Hazard Ratios (HR) (5 observations) which were assumed to

reasonably estimate the same association between CDI and PPIs

because of low CDI incidence and are pooled together. Adjusted

effect estimates were primarily used for this analysis. Unadjusted

effect estimates were used as alternatives if studies did not observe

an association on univariate comparison and did not therefore

pursue adjustment or did not report adjusted estimates. We

performed meta-analyses for all studies together and separately for

different subgroups such as case-control studies and cohort studies.

Effect estimates from all included studies were pooled in a meta-

analysis using the DerSimonian and Laird random effects model [29].

Exploring heterogeneity. Homogeneity among studies was

estimated by calculation of the variation across studies attributable

to heterogeneity rather than chance (I2). The influence of a range

of a-priori selected study-level and aggregated individual-level

parameters on the observed effect estimate was investigated by

means of meta-regressions. In these analyses, the log odds ratio

from each study was regressed on the potential confounders in

univariate and multivariate weighted linear regressions, weighted

according to the inverse standard error and the residual between-

study variance. Nine potential confounders were considered. Six

variables were categorical: design of the study (case-control vs.

cohort), country of publication, setting (single center vs. multicen-

ter), method of ascertainment of antibiotic use, method of effect

measure (OR vs. RR/HR) and effect estimate (adjusted vs.

unadjusted). Three continuous variables were: the impact factor of

the journal where the study was published, number of variables the

effect measure was adjusted for and proportion of cases that were

exposed to antibiotics.

Publication bias. The possible influence of publication bias

was graphically assessed with the novel method of contour-

enhanced funnel plot [30] where log-transformed odds ratios were

plotted against standard errors. This method examines whether

any funnel plot asymmetry is likely to be due to publication bias

compared with other underlying causes of funnel plot asymmetry.

The contours help to indicate whether areas of the plot, where

studies are perceived to be missing, are where studies would have

statistically significant effect sizes or not and thus decrease or

increase the evidence that the asymmetry is due to publication

bias. The presence of funnel plot asymmetry was also assessed

Proton Pump Inhibitors and C. difficile Infection


using Egger’s test [31]. To adjust for the impact of publication bias

on the pooled effect estimate, we used a novel regression based

adjustment method recently suggested by Moreno et al [32]. An

adjusted pooled effect estimate for an ideal study of a very large

size (i.e. with zero standard error) is obtained from the fitted

weighted linear regression equation, plotted with a regression line

on the contour enhanced funnel plot. This method of regression is

a modified version of conventional Egger’s regression test for

publication bias where the log of effect estimate is regressed by its

variance rather than the standard error and weights are assigned

according to the inverse of the variance. This model has been

shown to consistently outperform the conventional trim and fill

method [32].

Residual confounding. Finally, the possible influence of

unknown confounders (residual confounding) was investigated

with a rule-out approach described by Schneeweiss [33]. This

approach stipulates the influence of a hypothetical confounder and

determines what characteristics this confounder must have to fully

account for the observed association between use of PPIs and

occurrence of CDI. The hypothetical confounder is characterized

by its association to PPIs use (OREC, odds ratio of exposure to the

confounder) and its association to the outcome (RRCO, relative

risk of outcome in individuals exposed to the confounder vs. non-

exposed). For this analysis, the absolute risk in the pooled non-

exposed group was used for conversion of odds ratio to relative risk

using the method described by Zhang and Yu [34]. Separate

analyses were performed to demonstrate what levels of OREC and

RRCO would be required to fully explain the observed association

between PPIs and CDI for different hypothetical prevalence of the

unknown confounder (i.e. PC = 0.2, PC = 0.5) before and after

adjustment for publication bias as described above.

In all analyses, results associated with p-values ,0.05 (two-sided

test) were considered statistically significant. All statistical analyses

were performed using Stata version12 statistical software (Stata

Corp, College Station, Texas).

Results

Yield of Search Strategy and Eligible StudiesThe search strategy yielded 287 publications of which 242 were

not eligible for inclusion based on title/abstract review. Reference

lists of all eligible articles were systematically searched and 7

additional studies were identified that were not captured by our

search strategy. A total of 47 citations, 4 of which reported data on

4 different populations, that examined the association between PPI

therapy and CDI were eligible for this review. Figure 1

summarizes the study selection process and is presented in the

appendix.

Figure 1. Flow diagram of eligible studies.doi:10.1371/journal.pone.0050836.g001



Characteristics of the Included StudiesTable 1 summarizes characteristics of the included studies.

Studies were conducted in Asia, Europe, North America (Canada

and USA). One citation reported both a case-control and cohort

designs on 2 different patient populations [35]. Three citations

[36–38] reported two different case control analyses per each

citation. Forty analyses were single-centre, nine were multi-centre,

and two from a general practice research database (GPRD). Thirty

seven analyses were of case-control design and 14 were of cohort

design. Among these, 8 exclusively addressed community-acquired

CDI, 37 hospital-acquired and 6 both hospital- and community-

acquired CDI. Table S1 and S2 summarizes the CDI case

ascertainment and control or non-exposed group selection method

for all studies.

Quality Assessment of Included StudiesQuality assessment of all included studies was done using the

validated Newcastle-Ottawa Quality Assessment Scale [27] for

cohort and case control studies (Table S3 and Table S4). Most

studies were of good quality with no evidence of selection bias, and

with good comparability of the exposed and unexposed groups of

each cohort, and outcome assessment. Fifty-one individual effect

estimates from 47 eligible citations were extracted. We identified 2

outliers and excluded them from the final analyses as per the

Cochrane Handbook for Systematic Reviews [39]. The 2 outliers

were: Bajaj et al [40] because of a high OR = 37.6, and Wilcox

et al [41] because of large SE (SE log OR = 3.59). Final

interpretation was based on analyses of the 51 observations.

Meta-analysisAssociation between PPI and CDI. Fifty one individual

effect estimates from 47 eligible studies were extracted. Figure 2

shows the results of the pooled estimates for the 51 observations.

The pooled OR for the 51 observations was 1.65, 95% CI (1.47,

1.85), I2 = 89.9%. Table 2 summarizes the pooled estimates and

associated heterogeneity for different subgroups. All estimates

supported an association between PPI therapy and CDI.

Exploring heterogeneity. The influence of a range of a-

priori selected study-level and aggregated individual-level param-

eters on the observed effect estimate was investigated by means of

meta-regressions. Table S5 summarizes the meta-regression

analyses for all 51 results and is presented in the appendix. We

observed that studies that used interviews to ascertain PPI

exposure had on average lower effect estimates that studies that

used medical records 1.17 (0.91, 1.51) vs. 1.89 (1.45, 2.45), p for

interaction = 0.05 (Table 2). We also observed that studies that

used adjusted effect estimates [1.76 (95% CI, 1.54, 2.00)] had

higher pooled estimates than those that used unadjusted effect

estimates [1.27 (95% CI, 0.93, 1.72)], p = 0.07.

Publication bias. Figure 3 displays a contour-enhanced

funnel plot with the corresponding fixed (FE) and random effect

(RE) meta-analyses pooled estimates providing a weighted average

of effect size across studies of 1.02 (95% CI, 1.01–1.03) and 1.65

(95% CI, 1.47–1.85) respectively. There was visual evidence of

funnel asymmetry and Egger’s test for publication bias, P = 0.001.

Hence, a novel regression based method was used to adjust for

publication bias.

The fitted regression line plotted in Figure 3 corresponds to the

regression-based adjustment method. The adjusted estimate is

obtained by extrapolating the line with a standard error of 0 (at the

top of the funnel plot). This produced an adjusted average effect

estimate (RE model) of 1.51 (95% CI, 1.26–1.83).

Residual confounding. The results of the residual confound-

ing analysis are presented in Figure 4. Panel A refers to a

confounder with a prevalence of 0.20 and at this prevalence level,

even a strong confounder causing a 2.5-fold increased risk of CDI

would have to be imbalanced between acid-suppression users and

non-users (OREC = 3.8) to fully account for the observed adjusted

RR of 1.32 (adjusted for publication bias). For a very common

confounder with a prevalence of 0.50 (Panel B) and causing a 2.5-

fold increased risk of CDI, it would have to be distinctly

imbalanced between acid-suppression users and non-users

(OREC = 5.38) to fully account for the observed adjusted RR of

1.32.

Number Needed to HarmThe number needed to harm (NNH) was estimated by using the

pooled OR from the meta-analysis [42]. This analysis is only

speculative as it assumes there is a cause-effect relationship

between PPI and CDI. A recent large prospective hospital cohort

[43] reported the incidence of CDI at 14 days after hospital

admission in patients receiving antibiotics or not: which was 42/

1,000 and 5.4/1000, respectively. Based on these reported baseline

risks, the number needed to harm (NNH) was 50, 95% CI (31, 97)

and 367, 95% CI (226, 718), respectively. For the general

population, the NNH at 1 year was 3925, 95% CI (2412, 7698) at

1 year, based on a baseline incidence of CDI of 48/100,000

person-years [39].

Discussion

FindingsIn this rigorously conducted systemic review and meta-analysis,

we observed a weak association between PPI use and risk of CDI.

This association was further weakened by the presence of

significant heterogeneity. Although we adjusted for publication

bias and ruled out a strong effect of an unmeasured confounder,

the cumulative evidence provided by this systematic review

constitutes only very low quality evidence (as per GRADE

framework) in favor of this association. Factors that negatively

influence the quality of the evidence include the observational

design, inconsistency of results, and evidence of publication bias.

Moreover, even if we assume that the pooled effect estimate is valid, the

absolute risk of CDI would be very low in the general population

with an estimated NNH of 3925 at 1 year. In contrast, the risk

would behighest in hospitalized patients receiving antibiotics with

an estimated NNH of 50 at 2 weeks.

Comparison to Other StudiesSeveral systematic reviews [15,21–23,44,45] examining this

association have been published previously; however, our review is

the most comprehensive and is unique in its analytical approach

and interpretation and thus adds substantially to the cumulative

evidence. Table 3 summarizes the differences between our

systematic and those recently published. First, our review

identified the largest number of studies published to date. For

example, our review has 36% more studies than the largest meta-

analysis by Kowk et al [21] and 90% more studies that the recent

FDA review published in February 2012. Second, our meta-

analysis included adjusted effect estimates of the association

between PPI and CDI. Third, we used meta-regression to explore

sources of heterogeneity. None of the published analyses used this

method. Fourth, we examined the effect of publication bias using a

novel approach of contour-enhanced funnel plot [46]. The largest

and most recent analysis by Kowk et al. [21] did not examine the

effect of publication bias. Fifth, we used a novel regression-based

method to adjust the pooled estimate for publication bias and we



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examined the potential effect of a residual confounding on the

observed association using the rule-out approach.

Finally, we interpret the results in the context of observed

limitations and therefore, draw more careful conclusions. Contrary

to other reviews and FDA alert, we conclude that current

cumulative data constitutes very low quality evidence. Our results

are helpful for guidelines writing committees and policy makers

that use the GRADE framework when formulating recommenda-

tions for use of PPI for different clinical indications.

Biologic PlausibilityThe mechanism by which PPI therapy contributes to an

increased risk of CDI is unclear, because gastric acid does not kill

Figure 2. Forest Plot of the Meta analyses of The Association Between CDI and Proton Pump Inhibitors Based on 51 Observations.doi:10.1371/journal.pone.0050836.g002



gastric C. difficile spores. This further makes a cause-effect

relationship a less likely explanation for the observed association.

It has been proposed that the vegetative form of C. difficile,

which is killed by acid, plays a role in pathogenesis. Vegetative

forms survive on surfaces and could be ingested by patients [47].

Survival of these acid-sensitive vegetative forms in the stomach

could be facilitated by 2 main factors: (1) suppression of gastric

acid production by acid-suppressive medications; and (2) presence

of bile salts in gastric contents of patients on acid-suppressive

therapy. Bile salts, which are mainly found in the small intestine,

are present in gastric contents, particularly among patients with

gastro-esophageal reflux disease (GERD). Moreover, PPI use can

delay gastric emptying and predispose to bacterial overgrowth

with associated high intragastric bile salts which could trigger

spore germination in the stomach [48–50].

However, a recent in vitro experiment has challenged these

postulated biological mechanisms for the observed association. In

this experiment, aspirate of gastric contents from hospitalized

patients with nasogastric tubes were collected. It concluded that C.

difficile spores were not killed in acidic gastric content and did not

Table 2. Influence of study type, country, weather effect estimate adjusted or not and PPI ascertainment method on the pooledeffect estimate and its associated heterogeneity.

Group Pooled Effect Estimate 95% CI I2% Number of Observations

All citations 1.65 (1.47, 1.85) 89.9 51

Case-control citations 1.70 (1.42, 2.03) 88.7 37

Cohort citations 1.64 (1.30, 2.08) 87.8 14

Asia 3.26(1.91, 5.58) 0.0 3

Canada 1.22 (1.09, 1.37) 82.1 14

Europe 1.90 (1.35, 2.66) 75.5 10

USA 1.70 (1.41, 2.04) 73.3 24

Studies reported adjusted effect estimates* 1.78 (1.56, 2.02) 92.2 37

Studies reported unadjusted effect estimates* 1.27 (0.93, 1.71) 59.4 14

PPI ascertainment method (Chart){ 1.89 (1.45, 2.45) 93.6 20

PPI ascertainment method (Interview){ 1.17 (0.91, 1.51) 79.0 8

doi:10.1371/journal.pone.0050836.t002

Figure 3. Contour enhanced funnel plot of the association between the effect-estimates and its standard errors: * Contour enhancedfunnel plots with implementation of regression adjustment model (adjusted effect at top where SE is 0).* The contour lines differentiate thesignificance and non-significance regions in the plot at 1%, 5% and 10% significance levels. *Vertical lines show average effect-estimates fromrandom effect (red), and fixed effect models (blue). *A regression line (black) is added for regression based adjustment (With adjusted effect estimateand 95% CI at top where SE is 0). Abbreviations: FEMA: Fixed effect meta-analysis, REMA: Random effect meta-analysis, Reg: Regression line.doi:10.1371/journal.pone.0050836.g003



Figure 4. Influence of a hypothetical dichotomous confounder present in 20% (panel A) and 50% (panel B) of the study population,unaccounted for in the adjustments already performed in the individual studies. The graphs indicate what combinations of OREC and RRthat would be necessary for the confounder to fully account for the observed association between proton pump inhibitor (PPI) use and CDAD afteradjustment for publication bias. Abbreviations: OREC, odds ratio of exposure to the confounder in PPI non-users vs. acid-suppression users; RRCD,relative risk of CDAD in individuals exposed to the confounder vs. non-exposed.doi:10.1371/journal.pone.0050836.g004



germinate in gastric contents of hospitalized patients on PPI.

Germination occurred with the addition of taurocholic acid [51].

LimitationsThere are limitations to our work. First, observational studies

are subject to inherent limitations in the study design leading to

unmeasured differences in the study population and unmeasured

confounders despite all possible adjustments. PPIs use may be a

surrogate of comorbidities and thus, the observed association may

have been affected by selective overuse of PPIs in high risk groups.

For example, the potential interaction between PPIs and

Clopidogrel found in observational studies was refuted in

randomized controlled trials [52]. Second, the use of PPIs was

based on electronic and prescription records, rather than by actual

use by the patient. Third, there is presence of publication bias, and

substantial amount of heterogeneity in the included studies. There

are many patient level parameters which may have led to

substantial heterogeneity. Nevertheless, investigating these vari-

ables is only possible with individual patient data meta-analysis.

Fourth, all statistical methods used to assess for publication bias or

residual confounding are subject to certain assumptions and have

inherent limitations. For example, funnel plot asymmetry can be

due to between studies heterogeneity rather than publication bias

[31].

Given these limitations, focus on hand hygiene as one of the

cornerstones of prevention of nosocomial transmission of C. difficile

is warranted. Several studies have documented the reduction of

rates of hospital acquired infection by improvement in the

compliance with hand washing by healthcare workers between

episodes of contact with patients [53].

ConclusionsIn this rigorously conducted systemic review and meta-analysis,

we found very low quality evidence in support of an association

between PPI use and risk of CDI. This association was weakened

by the presence of significant heterogeneity and publication bias.

Our findings are re-assuring that PPIs use in the general

population does not pose a significant CDI risk. On the other

hand, our findings warrant judicious and evidence-based use of

PPI in patients at high risk for CDI.

Supporting Information

Table S1 The Association between PPI use and Devel-opment of Clostridium difficile infection in Case-controlCitations.

(DOCX)

Table S2 The Association between PPI use and Devel-opment of Clostridium difficile infection from Cohortcitations.

(DOCX)

Table S3 Meta-regression analysis to explore sources ofheterogeneity.

(DOCX)

Table S4 Forest plot of the meta-analysis of theproportion of Clostridium difficile cases that wereexposed to antibiotics.

(DOCX)

Table S5 Modified Newcastle-Ottawa Quality Assess-ment Scale for Case-control studies included in theMeta-analysis.

(DOCX)

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Figure S1 Forest plot of the meta-analysis of theproportion of Clostridium difficile cases that wereexposed to antibiotics.

(DOCX)

Author Contributions

Analyzed the data: IMT AJS MR ABA MAA MAG. Wrote the paper:

IMT MR ARK ABA MAA MAG MA. Study concept and design: IMT

AJS MR PJE. Data abstraction: ABA FAA MA AA TI MAG. Quality

assessment: FAA ABA MAG. Revision of the manuscript: IMT AJS MR

ABA FAA MAA AA TI MAG ARK LMB.

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